本文選題：慣性導航系統(tǒng) + 卡爾曼濾波��； 參考：《電子科技大學》2014年碩士論文

【摘要】：隨著技術(shù)的進步,原本應用于軍事領(lǐng)域的各種導航技術(shù)已應用于民用領(lǐng)域或正在向民用領(lǐng)域推廣。在民用領(lǐng)域,導航系統(tǒng)的安全性至關(guān)重要,完好性作為對導航系統(tǒng)安全性的一種衡量是評價一個導航系統(tǒng)優(yōu)劣的重要標準。自主導航是一種不借助人工設置的目標和信息源的導航儀器來引導物體運行的導航方法,慣性導航就是一種自主導航,包含慣性導航的組合導航也可以歸于自主導航。導航系統(tǒng)需要獲得測量信息進行導航解算,本文將重點研究如何通過故障檢測來提高導航系統(tǒng)的完好性。首先,對慣性導航系統(tǒng)進行仿真分析。慣性導航系統(tǒng)為了確定運動物體的初始姿態(tài)要先進行初始對準。初始對準包括粗對準和精對準,精對準用卡爾曼濾波器估計粗對準的誤差。本文提出了改進的精對準算法,利用兩個加速度計的輸出和陀螺儀的三個輸出共五個值作為卡爾曼濾波器的觀測值。建立數(shù)學模型模擬陀螺儀和加速度計的輸出,并搭建了慣性導航系統(tǒng)仿真平臺,對改進的精對準進行了仿真分析。仿真結(jié)果顯示改進的精對準算法提高了對方位誤差角的估計精度。其次,仿真分析了GPS導航系統(tǒng),并在慣性導航系統(tǒng)平臺上結(jié)合GPS導航系統(tǒng),分別仿真松組合導航和緊組合導航,對不同導航系統(tǒng)的仿真結(jié)果進行比較分析。搭建的仿真平臺為之后的故障檢測研究做鋪墊。然后,對故障檢測展開研究。首先介紹最優(yōu)奇偶矢量法,它是多故障檢測的基礎(chǔ),發(fā)現(xiàn)最優(yōu)奇偶矢量法存在缺陷,不同觀測值中的故障有可能使殘差變小,不利于對故障的檢測。為了解決這個問題,本文提出了改進的最優(yōu)奇偶矢量法,仿真比較傳統(tǒng)的方法和改進的方法對故障的檢測效果,仿真結(jié)果表明改進的最優(yōu)奇偶矢量法有效地解決了殘差變小的問題。實際情況中觀測精度可能不同,仿真比較殘差標準化法和加權(quán)奇偶矢量法對故障的檢測效果,分析加權(quán)奇偶矢量法的優(yōu)勢。另外本文發(fā)現(xiàn),當卡爾曼濾波器中待估值的個數(shù)遠遠大于觀測值的個數(shù)的時候,無法使用最優(yōu)奇偶矢量法檢測觀測方程中的故障,因此構(gòu)造新的統(tǒng)計量檢測其中的故障,仿真驗證了新的統(tǒng)計量對故障具有較好的檢測效果。最后,研究在GPS和組合導航系統(tǒng)中引入故障檢測的方法和可行性。仿真驗證了本文提出的故障檢測算法在這些導航系統(tǒng)中能有效地檢測出故障。
[Abstract]:With the development of technology, all kinds of navigation technology used in military field have been applied to civilian field or are being popularized to civilian field. In civil field, the security of navigation system is very important. As a measure of navigation system security, completeness is an important criterion to evaluate a navigation system. Autonomous navigation is a kind of navigation method which can guide the operation of objects without the aid of the navigation instrument of the target and information source. Inertial navigation is a kind of autonomous navigation, and the integrated navigation including inertial navigation can also be attributed to autonomous navigation. Navigation system needs to obtain measurement information for navigation solution. This paper will focus on how to improve the integrity of navigation system through fault detection. First, the inertial navigation system is simulated and analyzed. In order to determine the initial attitude of the moving object, the inertial navigation system needs initial alignment. The initial alignment consists of coarse alignment and fine alignment. Kalman filter is used to estimate the error of coarse alignment. In this paper, an improved precision alignment algorithm is proposed, which uses the output of two accelerometers and three outputs of gyroscopes as the observed values of Kalman filter. The mathematical model is established to simulate the output of gyroscope and accelerometer, and the simulation platform of inertial navigation system is built. The improved precision alignment is simulated and analyzed. Simulation results show that the improved precision alignment algorithm improves the precision of azimuth error angle estimation. Secondly, the GPS navigation system is simulated and analyzed, and the loose integrated navigation and compact integrated navigation are simulated on the inertial navigation system platform. The simulation results of different navigation systems are compared and analyzed. The simulation platform is used to pave the way for the later fault detection research. Then, the research on fault detection is carried out. This paper first introduces the optimal even-odd vector method, which is the basis of multi-fault detection. It is found that the optimal odd-even vector method has defects, and the fault in different observation values may make the residual error smaller, which is not conducive to fault detection. In order to solve this problem, an improved optimal even-odd vector method is proposed in this paper. The simulation results are compared with the traditional method and the improved method for fault detection. The simulation results show that the improved optimal even-odd vector method can effectively solve the problem of reducing the residual error. The actual observation accuracy may be different. The residual standardization method and the weighted odd-even vector method are compared in the fault detection effect, and the advantages of the weighted odd-even vector method are analyzed. In addition, it is found that when the number of Kalman filters to be estimated is far greater than the number of observed values, it is impossible to detect the faults in the observation equation by using the optimal even-odd vector method, so a new statistic is constructed to detect the faults. The simulation results show that the new statistic is effective for fault detection. Finally, the method and feasibility of introducing fault detection into GPS and integrated navigation system are studied. Simulation results show that the proposed fault detection algorithm can effectively detect faults in these navigation systems.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN96

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本文編號：2048125